CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Cross-Domain Collaborative Learning via Discriminative Non-parametric Bayesian Mode
Shengsheng Qian1,2; Tianzhu Zhang1,2; Changsheng Xu1,2
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2018
Volume20Issue:8Pages:2086-2099
Abstract

Cross-domain data analysis has been becoming more and more important, and can be effectively adopted for many applications. However, it is difficult to propose a unified crossdomain collaborative learning framework for cross domain analysis in social multimedia, because cross-domain data have multi-domain, multi-modal, sparse and supervised properties. In the paper, we propose a generic Cross-Domain Collaborative Learning (CDCL) framework via a discriminative nonparametric Bayesian dictionary learning model for cross-domain data analysis. Compared with existing cross-domain learning methods, our proposed model mainly has four advantages: (1) To address the domain discrepancy, we utilize the shared domain priors among multiple domains to make them share a common feature space. (2) To exploit the multi-modal property, we use the shared modality priors to model the relationship between different modalities. (3) To deal with the sparse property of media data in one domain, our goal is to learn a shared dictionary to bridge different domains and complement each other. (4) To make use of the supervised property, we exploit class label information to learn the shared discriminative dictionary, and utilize a latent probability vector to select different dictionary elements for representation of each class. Therefore, the proposed model can investigate the superiorities of different sources to supplement and improve each other effectively. In experiments, we have evaluated our model for two important applications including cross-platform event recognition and cross-network video recommendation. The experimental results have showed the effectiveness of our CDCL model for cross domain analysis.

KeywordSocial Media discriminative non-parametric Bayesian model multi-modality
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20464
Collection模式识别国家重点实验室_多媒体计算与图形学
Corresponding AuthorChangsheng Xu
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Shengsheng Qian,Tianzhu Zhang,Changsheng Xu. Cross-Domain Collaborative Learning via Discriminative Non-parametric Bayesian Mode[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2018,20(8):2086-2099.
APA Shengsheng Qian,Tianzhu Zhang,&Changsheng Xu.(2018).Cross-Domain Collaborative Learning via Discriminative Non-parametric Bayesian Mode.IEEE TRANSACTIONS ON MULTIMEDIA,20(8),2086-2099.
MLA Shengsheng Qian,et al."Cross-Domain Collaborative Learning via Discriminative Non-parametric Bayesian Mode".IEEE TRANSACTIONS ON MULTIMEDIA 20.8(2018):2086-2099.
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